Merge branch 'hepj-test' into 'main'

增加transformer-xl模型代码

See merge request dcutoolkit/deeplearing/dlexamples_new!23

TensorFlow/NLP/transformer-xl-master/sota/enwik8.sh

+#!/bin/bash
+
+# Data
+DATA_ROOT=./
+DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_enwik8/data
+MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_enwik8/model
+
+# Model
+N_LAYER=24
+D_MODEL=1024
+D_EMBED=1024
+N_HEAD=8
+D_HEAD=128
+D_INNER=3072
+
+# Testing
+TEST_TGT_LEN=128
+TEST_MEM_LEN=3800
+TEST_CLAMP_LEN=1000
+
+TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-0
+TEST_BSZ=16
+TEST_NUM_CORE=2
+
+
+echo 'Preprocess test set...'
+python data_utils.py \
+  --data_dir=${DATA_DIR}/ \
+  --dataset=enwik8 \
+  --tgt_len=${TEST_TGT_LEN} \
+  --per_host_test_bsz=${TEST_BSZ} \
+  --num_passes=1 \
+  --use_tpu=False
+
+echo 'Run evaluation on test set...'
+python train_gpu.py \
+    --data_dir=${DATA_DIR}/tfrecords \
+    --record_info_dir=${DATA_DIR}/tfrecords/ \
+    --corpus_info_path=${DATA_DIR}/corpus-info.json \
+    --eval_ckpt_path=${TEST_CKPT_PATH} \
+    --model_dir=EXP-enwik8 \
+    --n_layer=${N_LAYER} \
+    --d_model=${D_MODEL} \
+    --d_embed=${D_EMBED} \
+    --n_head=${N_HEAD} \
+    --d_head=${D_HEAD} \
+    --d_inner=${D_INNER} \
+    --dropout=0.0 \
+    --dropatt=0.0 \
+    --tgt_len=${TEST_TGT_LEN} \
+    --mem_len=${TEST_MEM_LEN} \
+    --clamp_len=${TEST_CLAMP_LEN} \
+    --same_length=True \
+    --eval_batch_size=${TEST_BSZ} \
+    --num_core_per_host=${TEST_NUM_CORE} \
+    --do_train=False \
+    --do_eval=True \
+    --eval_split=test

TensorFlow/NLP/transformer-xl-master/sota/lm1b.sh

+#!/bin/bash
+
+# Data
+DATA_ROOT=./
+DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_lm1b/data
+MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_lm1b/model
+
+# Model
+DIV_VAL=4
+N_LAYER=24
+D_MODEL=1280
+D_EMBED=1280
+N_HEAD=16
+D_HEAD=80
+D_INNER=8192
+
+# Testing
+TEST_TGT_LEN=32
+TEST_MEM_LEN=128
+TEST_CLAMP_LEN=-1
+
+TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-1191000
+TEST_BSZ=16
+TEST_NUM_CORE=1
+
+
+echo 'Preprocess test set...'
+python data_utils.py \
+    --data_dir=${DATA_DIR}/ \
+    --dataset=lm1b \
+    --tgt_len=${TEST_TGT_LEN} \
+    --per_host_test_bsz=${TEST_BSZ} \
+    --num_passes=1 \
+    --use_tpu=False
+
+echo 'Run evaluation on test set...'
+python train_gpu.py \
+    --data_dir=${DATA_DIR}/tfrecords \
+    --record_info_dir=${DATA_DIR}/tfrecords/ \
+    --corpus_info_path=${DATA_DIR}/corpus-info.json \
+    --eval_ckpt_path=${TEST_CKPT_PATH} \
+    --model_dir=EXP-lm1b \
+    --div_val=${DIV_VAL} \
+    --untie_r=True \
+    --proj_share_all_but_first=False \
+    --proj_same_dim=False \
+    --n_layer=${N_LAYER} \
+    --d_model=${D_MODEL} \
+    --d_embed=${D_EMBED} \
+    --n_head=${N_HEAD} \
+    --d_head=${D_HEAD} \
+    --d_inner=${D_INNER} \
+    --dropout=0.0 \
+    --dropatt=0.0 \
+    --tgt_len=${TEST_TGT_LEN} \
+    --mem_len=${TEST_MEM_LEN} \
+    --clamp_len=${TEST_CLAMP_LEN} \
+    --same_length=True \
+    --eval_batch_size=${TEST_BSZ} \
+    --num_core_per_host=${TEST_NUM_CORE} \
+    --do_train=False \
+    --do_eval=True \
+    --eval_split=test

TensorFlow/NLP/transformer-xl-master/sota/text8.sh

+#!/bin/bash
+
+# Data
+DATA_ROOT=./
+DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_text8/data
+MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_text8/model
+
+# Model
+N_LAYER=24
+D_MODEL=1024
+D_EMBED=1024
+N_HEAD=8
+D_HEAD=128
+D_INNER=3072
+
+# Testing
+TEST_TGT_LEN=128
+TEST_MEM_LEN=3800
+TEST_CLAMP_LEN=1000
+
+TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-0
+TEST_BSZ=16
+TEST_NUM_CORE=2
+
+
+echo 'Preprocess test set...'
+python data_utils.py \
+  --data_dir=${DATA_DIR}/ \
+  --dataset=text8 \
+  --tgt_len=${TEST_TGT_LEN} \
+  --per_host_test_bsz=${TEST_BSZ} \
+  --num_passes=1 \
+  --use_tpu=False
+
+echo 'Run evaluation on test set...'
+python train_gpu.py \
+    --data_dir=${DATA_DIR}/tfrecords \
+    --record_info_dir=${DATA_DIR}/tfrecords/ \
+    --corpus_info_path=${DATA_DIR}/corpus-info.json \
+    --eval_ckpt_path=${TEST_CKPT_PATH} \
+    --model_dir=EXP-text8 \
+    --n_layer=${N_LAYER} \
+    --d_model=${D_MODEL} \
+    --d_embed=${D_EMBED} \
+    --n_head=${N_HEAD} \
+    --d_head=${D_HEAD} \
+    --d_inner=${D_INNER} \
+    --dropout=0.0 \
+    --dropatt=0.0 \
+    --tgt_len=${TEST_TGT_LEN} \
+    --mem_len=${TEST_MEM_LEN} \
+    --clamp_len=${TEST_CLAMP_LEN} \
+    --same_length=True \
+    --eval_batch_size=${TEST_BSZ} \
+    --num_core_per_host=${TEST_NUM_CORE} \
+    --do_train=False \
+    --do_eval=True \
+    --eval_split=test

TensorFlow/NLP/transformer-xl-master/sota/wt103.sh

+#!/bin/bash
+
+# Data
+DATA_ROOT=./
+DATA_DIR=${DATA_ROOT}/pretrained_xl/tf_wt103/data
+MODEL_DIR=${DATA_ROOT}/pretrained_xl/tf_wt103/model
+
+# Model
+DIV_VAL=4
+N_LAYER=18
+D_MODEL=1024
+D_EMBED=1024
+N_HEAD=16
+D_HEAD=64
+D_INNER=4096
+
+# Training
+TGT_LEN=256
+MEM_LEN=256
+
+BSZ=16
+NUM_CORE=2
+
+# Testing
+TEST_TGT_LEN=128
+TEST_MEM_LEN=1600
+TEST_CLAMP_LEN=1000
+
+TEST_CKPT_PATH=${MODEL_DIR}/model.ckpt-0
+TEST_BSZ=16
+TEST_NUM_CORE=1
+
+
+echo 'Preprocess test set...'
+python data_utils.py \
+    --data_dir=${DATA_DIR}/ \
+    --dataset=enwik8 \
+    --tgt_len=${TEST_TGT_LEN} \
+    --per_host_test_bsz=${TEST_BSZ} \
+    --num_passes=1 \
+    --use_tpu=False
+
+
+echo 'Run evaluation on test set...'
+python train_gpu.py \
+    --data_dir=${DATA_DIR}/tfrecords \
+    --record_info_dir=${DATA_DIR}/tfrecords/ \
+    --corpus_info_path=${DATA_DIR}/corpus-info.json \
+    --eval_ckpt_path=${TEST_CKPT_PATH} \
+    --model_dir=EXP-wt103 \
+    --div_val=${DIV_VAL} \
+    --untie_r=True \
+    --proj_share_all_but_first=True \
+    --n_layer=${N_LAYER} \
+    --d_model=${D_MODEL} \
+    --d_embed=${D_EMBED} \
+    --n_head=${N_HEAD} \
+    --d_head=${D_HEAD} \
+    --d_inner=${D_INNER} \
+    --dropout=0.0 \
+    --dropatt=0.0 \
+    --tgt_len=${TEST_TGT_LEN} \
+    --mem_len=${TEST_MEM_LEN} \
+    --clamp_len=${TEST_CLAMP_LEN} \
+    --same_length=True \
+    --eval_batch_size=${TEST_BSZ} \
+    --num_core_per_host=${TEST_NUM_CORE} \
+    --do_train=False \
+    --do_eval=True \
+    --eval_split=test
+

TensorFlow/NLP/transformer-xl-master/test.sh

+#!/bin/bash
+./scripts/enwik8_base_test.sh train
+#mpirun --allow-run-as-root -np 3 scripts/enwik8_base_gpu.sh train

TensorFlow/NLP/transformer-xl-master/train.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import time
+
+from absl import flags
+import absl.logging as _logging  # pylint: disable=unused-import
+
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
+import tensorflow as tf
+from tensorflow.gfile import Exists as exists
+import model
+import data_utils
+import tpu_estimator
+
+import numpy as np
+from time import sleep
+
+
+# TPU parameters
+flags.DEFINE_string("master", default=None,
+                    help="master")
+flags.DEFINE_string("tpu", default=None,
+      help="The Cloud TPU to use for training. This should be either the name "
+      "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
+flags.DEFINE_string("gcp_project", default=None,
+      help="Project name for the Cloud TPU-enabled project. If not specified, "
+      "we will attempt to automatically detect the GCE project from metadata.")
+flags.DEFINE_string("tpu_zone",default=None,
+      help="GCE zone where the Cloud TPU is located in. If not specified, we "
+      "will attempt to automatically detect the GCE project from metadata.")
+flags.DEFINE_bool("use_tpu", default=True,
+      help="Use TPUs rather than plain CPUs.")
+flags.DEFINE_integer("num_hosts", default=1,
+      help="number of TPU hosts")
+flags.DEFINE_integer("num_core_per_host", default=8,
+      help="number of cores per host")
+
+# Experiment (data/checkpoint/directory) parameters
+flags.DEFINE_string("data_dir", default="",
+      help="Path to tf-records directory.")
+flags.DEFINE_string("record_info_dir", default="",
+      help="Path to local directory containing filenames.txt.")
+flags.DEFINE_string("corpus_info_path", default="",
+      help="Path to corpus-info.json file.")
+flags.DEFINE_string("model_dir", default=None,
+      help="Estimator model_dir.")
+flags.DEFINE_bool("do_eval", default=False,
+      help="Whether to run eval on the dev set.")
+flags.DEFINE_bool("track_mean", default=True,
+      help="Trace mean loss during training.")
+flags.DEFINE_string("eval_ckpt_path", None,
+      help="Checkpoint path for evaluation."
+           "If set, model_dir will be ignored."
+           "If unset, will use the latest ckpt in model_dir.")
+flags.DEFINE_string("warm_start_path", None,
+      help="Checkpoint path for warm start."
+           "If set, will clear Adam states."
+           "Note that the new model_dir should be different"
+           " from warm_start_path.")
+
+# Optimization paramenters
+flags.DEFINE_float("learning_rate", default=2.5e-4,
+      help="Maximum learning rate.")
+flags.DEFINE_float("clip", default=0.25,
+      help="Gradient clipping value.")
+# for cosine decay
+flags.DEFINE_float("min_lr_ratio", default=0.01,
+      help="Minimum ratio learning rate.")
+flags.DEFINE_integer("warmup_steps", default=0,
+      help="Number of steps for linear lr warmup.")
+
+# Training parameters
+flags.DEFINE_integer("train_batch_size", default=60,
+      help="Size of train batch.")
+flags.DEFINE_integer("eval_batch_size", default=60,
+      help="Size of valid batch.")
+flags.DEFINE_integer("train_steps", default=100000,
+      help="Total number of training steps.")
+flags.DEFINE_integer("iterations", default=500,
+      help="Number of iterations per repeat loop.")
+flags.DEFINE_integer("save_steps", default=10000,
+      help="number of steps for model checkpointing.")
+
+# Evaluation parameters
+flags.DEFINE_integer("max_eval_batch", default=-1,
+      help="Set -1 to turn off. Only used in test mode.")
+flags.DEFINE_bool("do_eval_only", default=False,
+      help="Run evaluation only.")
+flags.DEFINE_integer("start_eval_steps", default=10000,
+      help="Which checkpoint to start with in `do_eval_only` mode.")
+flags.DEFINE_string("eval_split", "valid",
+      help="Which data split to evaluate.")
+
+# Model paramenters
+flags.DEFINE_integer("tgt_len", default=70,
+      help="Number of steps to predict")
+flags.DEFINE_integer("mem_len", default=70,
+      help="Number of steps to cache")
+flags.DEFINE_bool("same_length", default=False,
+      help="Same length attention")
+flags.DEFINE_integer("clamp_len", default=-1,
+      help="Clamp length")
+
+flags.DEFINE_integer("n_layer", default=6,
+      help="Number of layers.")
+flags.DEFINE_integer("d_model", default=500,
+      help="Dimension of the model.")
+flags.DEFINE_integer("d_embed", default=500,
+      help="Dimension of the embeddings.")
+flags.DEFINE_integer("n_head", default=10,
+      help="Number of attention heads.")
+flags.DEFINE_integer("d_head", default=50,
+      help="Dimension of each attention head.")
+flags.DEFINE_integer("d_inner", default=1000,
+      help="Dimension of inner hidden size in positionwise feed-forward.")
+flags.DEFINE_float("dropout", default=0.1,
+      help="Dropout rate.")
+flags.DEFINE_float("dropatt", default=0.1,
+      help="Attention dropout rate.")
+flags.DEFINE_bool("untie_r", default=False,
+      help="untie r_w_bias and r_r_bias")
+
+# Adaptive Softmax / Embedding
+flags.DEFINE_bool("tie_weight", default=True,
+      help="Tie embedding and softmax weight.")
+flags.DEFINE_integer("div_val", default=1,
+      help="Divide the embedding size by this val for each bin")
+flags.DEFINE_bool("proj_share_all_but_first", default=False,
+      help="True to share all but first projs, False not to share.")
+flags.DEFINE_bool("proj_same_dim", default=True,
+      help="Project the bin with the same dimension.")
+
+# Parameter initialization
+flags.DEFINE_enum("init", default="normal",
+      enum_values=["normal", "uniform"],
+      help="Initialization method.")
+flags.DEFINE_float("init_std", default=0.02,
+      help="Initialization std when init is normal.")
+flags.DEFINE_float("proj_init_std", default=0.01,
+      help="Initialization std for embedding projection.")
+flags.DEFINE_float("init_range", default=0.1,
+      help="Initialization std when init is uniform.")
+
+
+FLAGS = flags.FLAGS
+
+def metric_fn(loss):
+  """Evaluation metric Fn which runs on CPU."""
+  perplexity = tf.exp(tf.reduce_mean(loss))
+  bpc = tf.reduce_mean(loss) / tf.constant(math.log(2))
+  return {
+      "perplexity": tf.metrics.mean(perplexity),
+      "bpc": tf.metrics.mean(bpc),
+  }
+
+
+def get_model_fn(n_token, cutoffs, train_bin_sizes, eval_bin_sizes):
+  def model_fn(features, labels, mode, params):
+    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
+
+
+    batch_size = params["batch_size"]
+
+    mems = params["cache"]
+    inp = tf.transpose(features["inputs"], [1, 0])
+    tgt = tf.transpose(features["labels"], [1, 0])
+
+    bin_sizes = train_bin_sizes if is_training else eval_bin_sizes
+    if bin_sizes:
+      inp_perms = [tf.transpose(features["inp_mask"], [1, 0])]
+      tgt_perms = [tf.transpose(features["tgt_mask"], [1, 0])]
+
+      head_tgt = tf.transpose(features["head_labels"], [1, 0])
+
+      for b in range(len(bin_sizes)):
+        inp_perm = tf.transpose(features["inp_perm_{}".format(b)], [1, 0, 2])
+        tgt_perm = tf.transpose(features["tgt_perm_{}".format(b)], [1, 0, 2])
+
+        inp_perms.append(inp_perm)
+        tgt_perms.append(tgt_perm)
+    else:
+      inp_perms, tgt_perms, head_tgt = None, None, None
+
+    if FLAGS.init == "uniform":
+      initializer = tf.initializers.random_uniform(
+          minval=-FLAGS.init_range,
+          maxval=FLAGS.init_range,
+          seed=None)
+    elif FLAGS.init == "normal":
+      initializer = tf.initializers.random_normal(
+          stddev=FLAGS.init_std,
+          seed=None)
+      proj_initializer = tf.initializers.random_normal(
+          stddev=FLAGS.proj_init_std,
+          seed=None)
+
+    tie_projs = [False for _ in range(len(cutoffs) + 1)]
+    if FLAGS.proj_share_all_but_first:
+      for i in range(1, len(tie_projs)):
+        tie_projs[i] = True
+
+    tf.logging.info("Vocab size : {}".format(n_token))
+    tf.logging.info("Batch size : {}".format(batch_size))
+
+    loss, new_mems = model.transformer(
+        dec_inp=inp,
+        target=tgt,
+        mems=mems,
+        n_token=n_token,
+        n_layer=FLAGS.n_layer,
+        d_model=FLAGS.d_model,
+        d_embed=FLAGS.d_embed,
+        n_head=FLAGS.n_head,
+        d_head=FLAGS.d_head,
+        d_inner=FLAGS.d_inner,
+        dropout=FLAGS.dropout,
+        dropatt=FLAGS.dropatt,
+        initializer=initializer,
+        is_training=is_training,
+        mem_len=FLAGS.mem_len,
+        cutoffs=cutoffs,
+        div_val=FLAGS.div_val,
+        tie_projs=tie_projs,
+        input_perms=inp_perms,
+        target_perms=tgt_perms,
+        head_target=head_tgt,
+        same_length=FLAGS.same_length,
+        clamp_len=FLAGS.clamp_len,
+        use_tpu=FLAGS.use_tpu,
+        untie_r=FLAGS.untie_r,
+        proj_same_dim=FLAGS.proj_same_dim)
+
+    total_loss = tf.reduce_mean(loss)
+
+    if mode == tf.estimator.ModeKeys.EVAL:
+      if FLAGS.use_tpu:
+        with tf.colocate_with(total_loss):
+          total_loss = tf.contrib.tpu.cross_replica_sum(total_loss) \
+                     / FLAGS.num_hosts / FLAGS.num_core_per_host
+      metric_loss = tf.tile(tf.reshape(total_loss, [1, 1]), [batch_size, 1])
+      eval_spec = tf.contrib.tpu.TPUEstimatorSpec(
+          mode=mode,
+          loss=total_loss,
+          eval_metrics=(metric_fn, [metric_loss]))
+
+      eval_spec.cache = new_mems
+
+      return eval_spec
+
+    # Configuring the optimization step.
+    global_step = tf.train.get_global_step()
+
+    # increase the learning rate linearly
+    if FLAGS.warmup_steps > 0:
+      warmup_lr = tf.to_float(global_step) / tf.to_float(FLAGS.warmup_steps) \
+                  * FLAGS.learning_rate
+    else:
+      warmup_lr = 0.0
+
+    # number of parameters
+    num_params = np.sum([np.prod(v.shape) for v in tf.trainable_variables()])
+    tf.logging.info("#params: {}".format(num_params))
+
+    # format_str = '{{:<{0}s}}\t{{}}'.format(
+    #     max([len(v.name) for v in tf.trainable_variables()]))
+    # for v in tf.trainable_variables():
+    #   tf.logging.info(format_str.format(v.name, v.get_shape()))
+
+
+    # decay the learning rate using the cosine schedule
+    decay_lr = tf.train.cosine_decay(
+        FLAGS.learning_rate,
+        global_step=global_step-FLAGS.warmup_steps,
+        decay_steps=FLAGS.train_steps-FLAGS.warmup_steps,
+        alpha=FLAGS.min_lr_ratio)
+
+    learning_rate = tf.where(global_step < FLAGS.warmup_steps,
+                             warmup_lr, decay_lr)
+
+    if FLAGS.use_tpu:
+      optimizer = tf.contrib.tpu.CrossShardOptimizer(
+          tf.train.AdamOptimizer(learning_rate=learning_rate))
+      #GradientDescentOptimizer
+    else:
+      optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+
+    grads_and_vars = optimizer.compute_gradients(total_loss)
+    gradients, variables = zip(*grads_and_vars)
+    clipped, _ = tf.clip_by_global_norm(gradients, FLAGS.clip)
+    train_op = optimizer.apply_gradients(
+        zip(clipped, variables), global_step=tf.train.get_global_step())
+
+    # Constucting TPUEstimatorSpec with cache.
+    train_spec = tf.contrib.tpu.TPUEstimatorSpec(
+        mode=mode, loss=total_loss, train_op=train_op)
+
+    if FLAGS.mem_len < FLAGS.tgt_len:
+      new_mems = [new_mems[: FLAGS.mem_len] for mem_t in new_mems]
+    train_spec.cache = new_mems
+
+    return train_spec
+
+  return model_fn
+
+
+def get_cache_fn(mem_len):
+
+  def cache_fn(batch_size):
+    mems = []
+    for l in xrange(FLAGS.n_layer):
+      if mem_len > 0:
+        mems.append(
+          tf.zeros([mem_len, batch_size, FLAGS.d_model], dtype=tf.float32))
+      else:
+        mems.append(tf.zeros([mem_len], dtype=tf.float32))
+
+    return mems
+
+  return cache_fn
+
+
+def main(unused_argv):
+  del unused_argv  # Unused
+
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  # Get corpus info
+  corpus_info = data_utils.get_corpus_info(FLAGS.corpus_info_path)
+  n_token = corpus_info["vocab_size"]
+  cutoffs = corpus_info["cutoffs"][1:-1]
+
+  if FLAGS.save_steps == 0:
+    FLAGS.save_steps = None
+
+  if not FLAGS.do_eval_only:
+    # Get train input function
+    train_input_fn, train_record_info = data_utils.get_input_fn(
+        record_info_dir=FLAGS.record_info_dir,
+        split="train",
+        per_host_bsz=FLAGS.train_batch_size // FLAGS.num_hosts,
+        tgt_len=FLAGS.tgt_len,
+        num_core_per_host=FLAGS.num_core_per_host,
+        num_hosts=FLAGS.num_hosts,
+        use_tpu=FLAGS.use_tpu)
+    train_bin_sizes = train_record_info["bin_sizes"]
+    num_train_batch = train_record_info["num_batch"]
+
+    # Get train cache function
+    train_cache_fn = get_cache_fn(FLAGS.mem_len)
+  else:
+    train_bin_sizes = []
+    num_train_batch = None
+    train_cache_fn = None
+
+  if FLAGS.do_eval or FLAGS.do_eval_only:
+    assert FLAGS.num_hosts == 1
+    # Get eval input function
+    eval_input_fn, eval_record_info = data_utils.get_input_fn(
+        record_info_dir=FLAGS.record_info_dir,
+        split=FLAGS.eval_split,
+        per_host_bsz=FLAGS.eval_batch_size // FLAGS.num_hosts,
+        tgt_len=FLAGS.tgt_len,
+        num_core_per_host=FLAGS.num_core_per_host,
+        num_hosts=FLAGS.num_hosts,
+        use_tpu=FLAGS.use_tpu)
+    eval_bin_sizes = eval_record_info["bin_sizes"]
+    num_eval_batch = eval_record_info["num_batch"]
+
+    if FLAGS.max_eval_batch > 0:
+      num_eval_batch = min(FLAGS.max_eval_batch, num_eval_batch)
+
+    # Get eval cache function
+    eval_cache_fn = get_cache_fn(FLAGS.mem_len)
+    model_fn = get_model_fn(n_token, cutoffs, train_bin_sizes, eval_bin_sizes)
+  else:
+    eval_cache_fn = None
+    model_fn = get_model_fn(n_token, cutoffs, train_bin_sizes, [])
+
+  ##### Create estimator
+  # TPU Configuration
+  tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
+      FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
+
+  per_host_input = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
+  run_config = tf.contrib.tpu.RunConfig(
+      cluster=tpu_cluster_resolver,
+      model_dir=FLAGS.model_dir,
+      session_config=tf.ConfigProto(
+          allow_soft_placement=True, log_device_placement=True),
+      tpu_config=tf.contrib.tpu.TPUConfig(
+          iterations_per_loop=FLAGS.iterations,
+          num_shards=FLAGS.num_core_per_host * FLAGS.num_hosts,
+          per_host_input_for_training=per_host_input),
+      keep_checkpoint_max=100000, # effectively save all checkpoints
+      save_checkpoints_secs=None,
+      save_checkpoints_steps=FLAGS.save_steps
+  )
+
+  # warm start
+  warm_start_from = None
+  if FLAGS.warm_start_path is not None:
+    warm_start_from = tf.estimator.WarmStartSettings(
+        ckpt_to_initialize_from=FLAGS.warm_start_path)
+
+  # TPU Estimator
+  estimator = tpu_estimator.TPUEstimator(
+      model_fn=model_fn,
+      train_cache_fn=train_cache_fn,
+      eval_cache_fn=eval_cache_fn,
+      use_tpu=FLAGS.use_tpu,
+      config=run_config,
+      params={"data_dir":FLAGS.data_dir, "track_mean":FLAGS.track_mean},
+      train_batch_size=FLAGS.train_batch_size,
+      eval_batch_size=FLAGS.eval_batch_size,
+      warm_start_from=warm_start_from)
+
+  if FLAGS.do_eval_only:
+    if FLAGS.eval_ckpt_path is not None:
+      ret = estimator.evaluate(input_fn=eval_input_fn, steps=num_eval_batch,
+                               checkpoint_path=FLAGS.eval_ckpt_path)
+      tf.logging.info("=" * 200)
+      log_str = "Eval results | "
+      for key, val in ret.items():
+        log_str += "{} {} | ".format(key, val)
+      tf.logging.info(log_str)
+      tf.logging.info("=" * 200)
+    else:
+      ckpt_state = tf.train.get_checkpoint_state(FLAGS.model_dir)
+      eval_results = []
+      for eval_checkpoint in ckpt_state.all_model_checkpoint_paths:
+        if not exists(eval_checkpoint + ".index"): continue
+        global_step = int(eval_checkpoint.split("-")[-1])
+        if global_step < FLAGS.start_eval_steps or global_step > FLAGS.train_steps:
+          continue
+        ret = estimator.evaluate(input_fn=eval_input_fn, steps=num_eval_batch,
+                                 checkpoint_path=eval_checkpoint)
+        eval_results.append(ret)
+
+      eval_results.sort(key = lambda x: x["perplexity"])
+
+      tf.logging.info("=" * 200)
+      log_str = "Best results | "
+      for key, val in eval_results[0].items():
+        log_str += "{} {} | ".format(key, val)
+      tf.logging.info(log_str)
+      tf.logging.info("=" * 200)
+  else:
+    if not FLAGS.do_eval:
+      estimator.train(input_fn=train_input_fn, steps=FLAGS.train_steps)
+    else:
+      for step in range(0, FLAGS.train_steps, num_train_batch):
+        train_steps = min(FLAGS.train_steps - step, num_train_batch)
+        estimator.train(input_fn=train_input_fn, steps=train_steps)
+        estimator.evaluate(input_fn=eval_input_fn, steps=num_eval_batch)
+
+
+if __name__ == "__main__":
+  tf.app.run()